{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# demo downloading data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "following icepyx tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from icepyx import icesat2data as ipd\n",
    "import os\n",
    "import shutil\n",
    "from pprint import pprint\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Whillans Ice Plain\n",
    "short_name = 'ATL06'\n",
    "spatial_extent = [-162, -85, -147, -83]\n",
    "date_range = ['2019-06-18','2019-06-25'] # leave some short range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATL06\n",
      "['2019-06-18', '2019-06-25']\n",
      "03:30:00\n",
      "21:30:00\n",
      "003\n",
      "['bounding box', [-162, -85, -147, -83]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/ipykernel_launcher.py:2: ResourceWarning: unclosed <socket.socket fd=56, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('172.16.6.156', 44328)>\n",
      "  \n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/ipykernel_launcher.py:2: ResourceWarning: unclosed <socket.socket fd=55, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('172.16.6.156', 60192), raddr=('128.138.97.100', 443)>\n",
      "  \n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
     ]
    }
   ],
   "source": [
    "region_a = ipd.Icesat2Data(short_name, spatial_extent, date_range, \\\n",
    "                           start_time='03:30:00', end_time='21:30:00')#, version='001')\n",
    "\n",
    "print(region_a.dataset)\n",
    "print(region_a.dates)\n",
    "print(region_a.start_time)\n",
    "print(region_a.end_time)\n",
    "print(region_a.dataset_version)\n",
    "print(region_a.spatial_extent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "region_a = ipd.Icesat2Data(short_name, spatial_extent, date_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATL06\n",
      "['2019-06-18', '2019-06-25']\n",
      "00:00:00\n",
      "23:59:59\n",
      "003\n",
      "['bounding box', [-162, -85, -147, -83]]\n"
     ]
    }
   ],
   "source": [
    "print(region_a.dataset)\n",
    "print(region_a.dates)\n",
    "print(region_a.start_time)\n",
    "print(region_a.end_time)\n",
    "print(region_a.dataset_version)\n",
    "print(region_a.spatial_extent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "region_a.visualize_spatial_extent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "003\n",
      "dataset_id :  ATLAS/ICESat-2 L3A Land Ice Height V003\n",
      "short_name :  ATL06\n",
      "version_id :  003\n",
      "time_start :  2018-10-14T00:00:00.000Z\n",
      "coordinate_system :  CARTESIAN\n",
      "summary :  This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.\n",
      "orbit_parameters :  {'swath_width': '36.0', 'period': '94.29', 'inclination_angle': '92.0', 'number_of_orbits': '0.071428571', 'start_circular_latitude': '0.0'}\n"
     ]
    }
   ],
   "source": [
    "print(region_a.latest_version())\n",
    "region_a.dataset_summary_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'feed': {'entry': [{'archive_center': 'NASA NSIDC DAAC',\n",
      "                     'associations': {'services': ['S1568899363-NSIDC_ECS',\n",
      "                                                   'S1613669681-NSIDC_ECS',\n",
      "                                                   'S1613689509-NSIDC_ECS']},\n",
      "                     'boxes': ['-90 -180 90 180'],\n",
      "                     'browse_flag': False,\n",
      "                     'coordinate_system': 'CARTESIAN',\n",
      "                     'data_center': 'NSIDC_ECS',\n",
      "                     'dataset_id': 'ATLAS/ICESat-2 L3A Land Ice Height V002',\n",
      "                     'has_formats': True,\n",
      "                     'has_spatial_subsetting': True,\n",
      "                     'has_temporal_subsetting': True,\n",
      "                     'has_transforms': False,\n",
      "                     'has_variables': True,\n",
      "                     'id': 'C1631076765-NSIDC_ECS',\n",
      "                     'links': [{'href': 'https://n5eil01u.ecs.nsidc.org/ATLAS/ATL06.002/',\n",
      "                                'hreflang': 'en-US',\n",
      "                                'length': '0.0KB',\n",
      "                                'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n",
      "                               {'href': 'https://search.earthdata.nasa.gov/search/granules?p=C1631076765-NSIDC_ECS&q=atl06%20v002&m=-113.62703547966265!-24.431396484375!0!1!0!0%2C2&tl=1556125020!4',\n",
      "                                'hreflang': 'en-US',\n",
      "                                'length': '0.0KB',\n",
      "                                'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n",
      "                               {'href': 'https://openaltimetry.org/',\n",
      "                                'hreflang': 'en-US',\n",
      "                                'length': '0.0KB',\n",
      "                                'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n",
      "                               {'href': 'https://doi.org/10.5067/ATLAS/ATL06.002',\n",
      "                                'hreflang': 'en-US',\n",
      "                                'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n",
      "                               {'href': 'https://doi.org/10.5067/ATLAS/ATL06.002',\n",
      "                                'hreflang': 'en-US',\n",
      "                                'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'}],\n",
      "                     'online_access_flag': True,\n",
      "                     'orbit_parameters': {'inclination_angle': '92.0',\n",
      "                                          'number_of_orbits': '0.071428571',\n",
      "                                          'period': '94.29',\n",
      "                                          'start_circular_latitude': '0.0',\n",
      "                                          'swath_width': '36.0'},\n",
      "                     'organizations': ['NASA NSIDC DAAC',\n",
      "                                       'NASA/GSFC/EOS/ESDIS'],\n",
      "                     'original_format': 'ISO19115',\n",
      "                     'processing_level_id': 'Level 3',\n",
      "                     'short_name': 'ATL06',\n",
      "                     'summary': 'This data set (ATL06) provides geolocated, '\n",
      "                                'land-ice surface heights (above the WGS 84 '\n",
      "                                'ellipsoid, ITRF2014 reference frame), plus '\n",
      "                                'ancillary parameters that can be used to '\n",
      "                                'interpret and assess the quality of the '\n",
      "                                'height estimates. The data were acquired by '\n",
      "                                'the Advanced Topographic Laser Altimeter '\n",
      "                                'System (ATLAS) instrument on board the Ice, '\n",
      "                                'Cloud and land Elevation Satellite-2 '\n",
      "                                '(ICESat-2) observatory.',\n",
      "                     'time_start': '2018-10-14T00:00:00.000Z',\n",
      "                     'title': 'ATLAS/ICESat-2 L3A Land Ice Height V002',\n",
      "                     'version_id': '002'},\n",
      "                    {'archive_center': 'NASA NSIDC DAAC',\n",
      "                     'associations': {'services': ['S1568899363-NSIDC_ECS',\n",
      "                                                   'S1613669681-NSIDC_ECS',\n",
      "                                                   'S1613689509-NSIDC_ECS']},\n",
      "                     'boxes': ['-90 -180 90 180'],\n",
      "                     'browse_flag': False,\n",
      "                     'coordinate_system': 'CARTESIAN',\n",
      "                     'data_center': 'NSIDC_ECS',\n",
      "                     'dataset_id': 'ATLAS/ICESat-2 L3A Land Ice Height V003',\n",
      "                     'has_formats': True,\n",
      "                     'has_spatial_subsetting': True,\n",
      "                     'has_temporal_subsetting': True,\n",
      "                     'has_transforms': False,\n",
      "                     'has_variables': True,\n",
      "                     'id': 'C1706333750-NSIDC_ECS',\n",
      "                     'links': [{'href': 'https://n5eil01u.ecs.nsidc.org/ATLAS/ATL06.003/',\n",
      "                                'hreflang': 'en-US',\n",
      "                                'length': '0.0KB',\n",
      "                                'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n",
      "                               {'href': 'https://search.earthdata.nasa.gov/search/granules?p=C1706333750-NSIDC_ECS&q=atl06%20v003&m=-29.109278436791882!-59.86889648437499!1!1!0!0%2C2&tl=1572814258!4!!',\n",
      "                                'hreflang': 'en-US',\n",
      "                                'length': '0.0KB',\n",
      "                                'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n",
      "                               {'href': 'https://openaltimetry.org/',\n",
      "                                'hreflang': 'en-US',\n",
      "                                'length': '0.0KB',\n",
      "                                'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n",
      "                               {'href': 'https://doi.org/10.5067/ATLAS/ATL06.003',\n",
      "                                'hreflang': 'en-US',\n",
      "                                'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n",
      "                               {'href': 'https://doi.org/10.5067/ATLAS/ATL06.003',\n",
      "                                'hreflang': 'en-US',\n",
      "                                'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'}],\n",
      "                     'online_access_flag': True,\n",
      "                     'orbit_parameters': {'inclination_angle': '92.0',\n",
      "                                          'number_of_orbits': '0.071428571',\n",
      "                                          'period': '94.29',\n",
      "                                          'start_circular_latitude': '0.0',\n",
      "                                          'swath_width': '36.0'},\n",
      "                     'organizations': ['NASA NSIDC DAAC',\n",
      "                                       'NASA/GSFC/EOS/ESDIS'],\n",
      "                     'original_format': 'ISO19115',\n",
      "                     'processing_level_id': 'Level 3',\n",
      "                     'short_name': 'ATL06',\n",
      "                     'summary': 'This data set (ATL06) provides geolocated, '\n",
      "                                'land-ice surface heights (above the WGS 84 '\n",
      "                                'ellipsoid, ITRF2014 reference frame), plus '\n",
      "                                'ancillary parameters that can be used to '\n",
      "                                'interpret and assess the quality of the '\n",
      "                                'height estimates. The data were acquired by '\n",
      "                                'the Advanced Topographic Laser Altimeter '\n",
      "                                'System (ATLAS) instrument on board the Ice, '\n",
      "                                'Cloud and land Elevation Satellite-2 '\n",
      "                                '(ICESat-2) observatory.',\n",
      "                     'time_start': '2018-10-14T00:00:00.000Z',\n",
      "                     'title': 'ATLAS/ICESat-2 L3A Land Ice Height V003',\n",
      "                     'version_id': '003'}],\n",
      "          'id': 'https://cmr.earthdata.nasa.gov:443/search/collections.json?short_name=ATL06',\n",
      "          'title': 'ECHO dataset metadata',\n",
      "          'updated': '2020-06-15T21:52:38.995Z'}}\n"
     ]
    }
   ],
   "source": [
    "region_a.dataset_all_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'short_name': 'ATL06',\n",
       " 'version': '003',\n",
       " 'temporal': '2019-06-18T00:00:00Z,2019-06-25T23:59:59Z',\n",
       " 'bounding_box': '-162,-85,-147,-83'}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build and view the parameters that will be submitted in our query\n",
    "region_a.CMRparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ATL06_20190618005217_12330311_003_01.h5',\n",
       " 'ATL06_20190618150054_12420311_003_01.h5',\n",
       " 'ATL06_20190619002638_12480311_003_01.h5',\n",
       " 'ATL06_20190619143514_12570311_003_01.h5',\n",
       " 'ATL06_20190620000058_12630311_003_01.h5',\n",
       " 'ATL06_20190620154352_12730311_003_01.h5',\n",
       " 'ATL06_20190621010937_12790311_003_01.h5',\n",
       " 'ATL06_20190621151813_12880311_003_01.h5',\n",
       " 'ATL06_20190622004357_12940311_003_01.h5',\n",
       " 'ATL06_20190622145233_13030311_003_01.h5',\n",
       " 'ATL06_20190623001818_13090311_003_01.h5',\n",
       " 'ATL06_20190623142654_13180311_003_01.h5',\n",
       " 'ATL06_20190623235238_13240311_003_01.h5',\n",
       " 'ATL06_20190624153532_13340311_003_01.h5',\n",
       " 'ATL06_20190625010116_13400311_003_01.h5',\n",
       " 'ATL06_20190625150952_13490311_003_01.h5']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get a list of the available granule IDs that meet your search criteria\n",
    "region_a.avail_granules(ids=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'producer_granule_id': 'ATL06_20190618005217_12330311_003_01.h5',\n",
       "  'time_start': '2019-06-18T00:52:17.000Z',\n",
       "  'orbit': {'ascending_crossing': '60.8647065253135',\n",
       "   'start_lat': '-79',\n",
       "   'start_direction': 'D',\n",
       "   'end_lat': '-79',\n",
       "   'end_direction': 'A'},\n",
       "  'updated': '2020-05-04T15:33:22.222Z',\n",
       "  'orbit_calculated_spatial_domains': [{'equator_crossing_date_time': '2019-06-17T23:44:28.789Z',\n",
       "    'equator_crossing_longitude': '60.8647065253135',\n",
       "    'orbit_number': '4208'}],\n",
       "  'dataset_id': 'ATLAS/ICESat-2 L3A Land Ice Height V003',\n",
       "  'data_center': 'NSIDC_ECS',\n",
       "  'title': 'SC:ATL06.003:178142590',\n",
       "  'coordinate_system': 'ORBIT',\n",
       "  'time_end': '2019-06-18T00:58:01.000Z',\n",
       "  'id': 'G1726183561-NSIDC_ECS',\n",
       "  'original_format': 'ISO-SMAP',\n",
       "  'granule_size': '101.0972499847',\n",
       "  'browse_flag': True,\n",
       "  'polygons': [['-78.77553376089676 -145.37976723821464 -81.77903098695559 55.48266494106754 -79.03276880301965 51.993841319723224 -78.97350320387933 53.66342278781005 -81.70034256182335 57.67000470149312 -78.7176025327635 -147.01301260580536 -78.77553376089676 -145.37976723821464']],\n",
       "  'collection_concept_id': 'C1706333750-NSIDC_ECS',\n",
       "  'online_access_flag': True,\n",
       "  'links': [{'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n",
       "    'type': 'application/x-hdfeos',\n",
       "    'hreflang': 'en-US',\n",
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       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
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       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
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       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190618005217_12330311_003_01_BRW.gt1l.h_li_sigma.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
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       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190618005217_12330311_003_01_BRW.gt1l.signal_selection_source.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190618005217_12330311_003_01_BRW.gt1r.atl06_quality_summary.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
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       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190618005217_12330311_003_01_BRW.gt1r.h_li.jpg'},\n",
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       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
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       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
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       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190618005217_12330311_003_01_BRW.gt1r.signal_selection_source.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
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       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
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       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190618005217_12330311_003_01_BRW.gt2l.signal_selection_source.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
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       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
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       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
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       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190618005217_12330311_003_01_BRW.gt3l.signal_selection_source.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190618005217_12330311_003_01_BRW.gt3r.atl06_quality_summary.jpg'},\n",
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       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt2l.atl06_quality_summary.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt2l.h_li.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt2l.h_li_sigma.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt2l.n_fit_photons.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt2l.signal_selection_source.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt2r.atl06_quality_summary.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt2r.h_li.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt2r.h_li_sigma.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt2r.n_fit_photons.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt2r.signal_selection_source.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt3l.atl06_quality_summary.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt3l.h_li.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt3l.h_li_sigma.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt3l.n_fit_photons.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt3l.signal_selection_source.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt3r.atl06_quality_summary.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt3r.h_li.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt3r.h_li_sigma.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt3r.n_fit_photons.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n",
       "    'type': 'image/jpeg',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP0/BRWS/Browse.001/2020.04.22/ATL06_20190625150952_13490311_003_01_BRW.gt3r.signal_selection_source.jpg'},\n",
       "   {'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n",
       "    'type': 'text/xml',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/DP7/ATLAS/ATL06.003/2019.06.25/ATL06_20190625150952_13490311_003_01.iso.xml'},\n",
       "   {'inherited': True,\n",
       "    'length': '0.0KB',\n",
       "    'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://n5eil01u.ecs.nsidc.org/ATLAS/ATL06.003/'},\n",
       "   {'inherited': True,\n",
       "    'length': '0.0KB',\n",
       "    'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://search.earthdata.nasa.gov/search/granules?p=C1706333750-NSIDC_ECS&q=atl06%20v003&m=-29.109278436791882!-59.86889648437499!1!1!0!0%2C2&tl=1572814258!4!!'},\n",
       "   {'inherited': True,\n",
       "    'length': '0.0KB',\n",
       "    'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://openaltimetry.org/'},\n",
       "   {'inherited': True,\n",
       "    'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://doi.org/10.5067/ATLAS/ATL06.003'},\n",
       "   {'inherited': True,\n",
       "    'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#',\n",
       "    'hreflang': 'en-US',\n",
       "    'href': 'https://doi.org/10.5067/ATLAS/ATL06.003'}]}]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print detailed information about the returned search results\n",
    "region_a.granules.avail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Earthdata Login password:  ·············\n"
     ]
    }
   ],
   "source": [
    "# Fill this in with your Earthdata Login user name and associated email\n",
    "\n",
    "earthdata_uid = 'cgbarcheck'\n",
    "email = 'grace.barcheck@cornell.edu'\n",
    "region_a.earthdata_login(earthdata_uid, email)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'page_size': 10, 'page_num': 2}\n"
     ]
    }
   ],
   "source": [
    "print(region_a.reqparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': '2019-06-18T00:00:00,2019-06-25T23:59:59',\n",
       " 'bbox': '-162,-85,-147,-83'}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_a.subsetparams()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# region_a.show_custom_options(dictview=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "var_list inputs: atlas_sdp_gps_epoch, control, data_end_utc, data_start_utc, end_cycle, end_delta_time, end_geoseg, end_gpssow, end_gpsweek, end_orbit, end_region, end_rgt, granule_end_utc, granule_start_utc, qa_at_interval, release, start_cycle, start_delta_time, start_geoseg, start_gpssow, start_gpsweek, start_orbit, start_region, start_rgt, version, dt_hist, fit_maxiter, fpb_maxiter, maxiter, max_res_ids, min_dist, min_gain_th, min_n_pe, min_n_sel, min_signal_conf, n_hist, nhist_bins, n_sigmas, proc_interval, qs_lim_bsc, qs_lim_hrs, qs_lim_hsigma, qs_lim_msw, qs_lim_snr, qs_lim_sss, rbin_width, sigma_beam, sigma_tx, t_dead, atl06_quality_summary, delta_time, h_li, h_li_sigma, latitude, longitude, segment_id, sigma_geo_h, fpb_mean_corr, fpb_mean_corr_sigma, fpb_med_corr, fpb_med_corr_sigma, fpb_n_corr, med_r_fit, tx_mean_corr, tx_med_corr, dem_flag, dem_h, geoid_h, dh_fit_dx, dh_fit_dx_sigma, dh_fit_dy, h_expected_rms, h_mean, h_rms_misfit, h_robust_sprd, n_fit_photons, n_seg_pulses, sigma_h_mean, signal_selection_source, signal_selection_source_status, snr, snr_significance, w_surface_window_final, bckgrd, bsnow_conf, bsnow_h, bsnow_od, cloud_flg_asr, cloud_flg_atm, dac, e_bckgrd, layer_flag, msw_flag, neutat_delay_total, r_eff, solar_azimuth, solar_elevation, tide_earth, tide_equilibrium, tide_load, tide_ocean, tide_pole, ref_azimuth, ref_coelv, seg_azimuth, sigma_geo_at, sigma_geo_r, sigma_geo_xt, x_atc, y_atc, bckgrd_per_m, bin_top_h, count, ds_segment_id, lat_mean, lon_mean, pulse_count, segment_id_list, x_atc_mean, record_number, reference_pt_lat, reference_pt_lon, signal_selection_status_all, signal_selection_status_backup, signal_selection_status_confident, crossing_time, cycle_number, lan, orbit_number, rgt, sc_orient, sc_orient_time, qa_granule_fail_reason, qa_granule_pass_fail, signal_selection_source_fraction_0, signal_selection_source_fraction_1, signal_selection_source_fraction_2, signal_selection_source_fraction_3\n",
      "keyword_list and beam_list inputs: ancillary_data, bias_correction, dem, fit_statistics, geophysical, ground_track, gt1l, gt1r, gt2l, gt2r, gt3l, gt3r, land_ice, land_ice_segments, none, orbit_info, quality_assessment, residual_histogram, segment_quality, signal_selection_status\n"
     ]
    }
   ],
   "source": [
    "region_a.order_vars.avail(options=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Default variables to order:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'atl06_quality_summary': ['gt1l/land_ice_segments/atl06_quality_summary',\n",
      "                           'gt1r/land_ice_segments/atl06_quality_summary',\n",
      "                           'gt2l/land_ice_segments/atl06_quality_summary',\n",
      "                           'gt2r/land_ice_segments/atl06_quality_summary',\n",
      "                           'gt3l/land_ice_segments/atl06_quality_summary',\n",
      "                           'gt3r/land_ice_segments/atl06_quality_summary'],\n",
      " 'atlas_sdp_gps_epoch': ['ancillary_data/atlas_sdp_gps_epoch'],\n",
      " 'bsnow_conf': ['gt1l/land_ice_segments/geophysical/bsnow_conf',\n",
      "                'gt1r/land_ice_segments/geophysical/bsnow_conf',\n",
      "                'gt2l/land_ice_segments/geophysical/bsnow_conf',\n",
      "                'gt2r/land_ice_segments/geophysical/bsnow_conf',\n",
      "                'gt3l/land_ice_segments/geophysical/bsnow_conf',\n",
      "                'gt3r/land_ice_segments/geophysical/bsnow_conf'],\n",
      " 'bsnow_h': ['gt1l/land_ice_segments/geophysical/bsnow_h',\n",
      "             'gt1r/land_ice_segments/geophysical/bsnow_h',\n",
      "             'gt2l/land_ice_segments/geophysical/bsnow_h',\n",
      "             'gt2r/land_ice_segments/geophysical/bsnow_h',\n",
      "             'gt3l/land_ice_segments/geophysical/bsnow_h',\n",
      "             'gt3r/land_ice_segments/geophysical/bsnow_h'],\n",
      " 'cloud_flg_asr': ['gt1l/land_ice_segments/geophysical/cloud_flg_asr',\n",
      "                   'gt1r/land_ice_segments/geophysical/cloud_flg_asr',\n",
      "                   'gt2l/land_ice_segments/geophysical/cloud_flg_asr',\n",
      "                   'gt2r/land_ice_segments/geophysical/cloud_flg_asr',\n",
      "                   'gt3l/land_ice_segments/geophysical/cloud_flg_asr',\n",
      "                   'gt3r/land_ice_segments/geophysical/cloud_flg_asr'],\n",
      " 'cloud_flg_atm': ['gt1l/land_ice_segments/geophysical/cloud_flg_atm',\n",
      "                   'gt1r/land_ice_segments/geophysical/cloud_flg_atm',\n",
      "                   'gt2l/land_ice_segments/geophysical/cloud_flg_atm',\n",
      "                   'gt2r/land_ice_segments/geophysical/cloud_flg_atm',\n",
      "                   'gt3l/land_ice_segments/geophysical/cloud_flg_atm',\n",
      "                   'gt3r/land_ice_segments/geophysical/cloud_flg_atm'],\n",
      " 'data_end_utc': ['ancillary_data/data_end_utc'],\n",
      " 'data_start_utc': ['ancillary_data/data_start_utc'],\n",
      " 'delta_time': ['gt1l/land_ice_segments/delta_time',\n",
      "                'gt1l/residual_histogram/delta_time',\n",
      "                'gt1l/segment_quality/delta_time',\n",
      "                'gt1r/land_ice_segments/delta_time',\n",
      "                'gt1r/residual_histogram/delta_time',\n",
      "                'gt1r/segment_quality/delta_time',\n",
      "                'gt2l/land_ice_segments/delta_time',\n",
      "                'gt2l/residual_histogram/delta_time',\n",
      "                'gt2l/segment_quality/delta_time',\n",
      "                'gt2r/land_ice_segments/delta_time',\n",
      "                'gt2r/residual_histogram/delta_time',\n",
      "                'gt2r/segment_quality/delta_time',\n",
      "                'gt3l/land_ice_segments/delta_time',\n",
      "                'gt3l/residual_histogram/delta_time',\n",
      "                'gt3l/segment_quality/delta_time',\n",
      "                'gt3r/land_ice_segments/delta_time',\n",
      "                'gt3r/residual_histogram/delta_time',\n",
      "                'gt3r/segment_quality/delta_time',\n",
      "                'quality_assessment/gt1l/delta_time',\n",
      "                'quality_assessment/gt1r/delta_time',\n",
      "                'quality_assessment/gt2l/delta_time',\n",
      "                'quality_assessment/gt2r/delta_time',\n",
      "                'quality_assessment/gt3l/delta_time',\n",
      "                'quality_assessment/gt3r/delta_time'],\n",
      " 'dh_fit_dx': ['gt1l/land_ice_segments/fit_statistics/dh_fit_dx',\n",
      "               'gt1r/land_ice_segments/fit_statistics/dh_fit_dx',\n",
      "               'gt2l/land_ice_segments/fit_statistics/dh_fit_dx',\n",
      "               'gt2r/land_ice_segments/fit_statistics/dh_fit_dx',\n",
      "               'gt3l/land_ice_segments/fit_statistics/dh_fit_dx',\n",
      "               'gt3r/land_ice_segments/fit_statistics/dh_fit_dx'],\n",
      " 'dh_fit_dx_sigma': ['gt1l/land_ice_segments/fit_statistics/dh_fit_dx_sigma',\n",
      "                     'gt1r/land_ice_segments/fit_statistics/dh_fit_dx_sigma',\n",
      "                     'gt2l/land_ice_segments/fit_statistics/dh_fit_dx_sigma',\n",
      "                     'gt2r/land_ice_segments/fit_statistics/dh_fit_dx_sigma',\n",
      "                     'gt3l/land_ice_segments/fit_statistics/dh_fit_dx_sigma',\n",
      "                     'gt3r/land_ice_segments/fit_statistics/dh_fit_dx_sigma'],\n",
      " 'dh_fit_dy': ['gt1l/land_ice_segments/fit_statistics/dh_fit_dy',\n",
      "               'gt1r/land_ice_segments/fit_statistics/dh_fit_dy',\n",
      "               'gt2l/land_ice_segments/fit_statistics/dh_fit_dy',\n",
      "               'gt2r/land_ice_segments/fit_statistics/dh_fit_dy',\n",
      "               'gt3l/land_ice_segments/fit_statistics/dh_fit_dy',\n",
      "               'gt3r/land_ice_segments/fit_statistics/dh_fit_dy'],\n",
      " 'end_delta_time': ['ancillary_data/end_delta_time'],\n",
      " 'granule_end_utc': ['ancillary_data/granule_end_utc'],\n",
      " 'granule_start_utc': ['ancillary_data/granule_start_utc'],\n",
      " 'h_li': ['gt1l/land_ice_segments/h_li',\n",
      "          'gt1r/land_ice_segments/h_li',\n",
      "          'gt2l/land_ice_segments/h_li',\n",
      "          'gt2r/land_ice_segments/h_li',\n",
      "          'gt3l/land_ice_segments/h_li',\n",
      "          'gt3r/land_ice_segments/h_li'],\n",
      " 'h_li_sigma': ['gt1l/land_ice_segments/h_li_sigma',\n",
      "                'gt1r/land_ice_segments/h_li_sigma',\n",
      "                'gt2l/land_ice_segments/h_li_sigma',\n",
      "                'gt2r/land_ice_segments/h_li_sigma',\n",
      "                'gt3l/land_ice_segments/h_li_sigma',\n",
      "                'gt3r/land_ice_segments/h_li_sigma'],\n",
      " 'h_mean': ['gt1l/land_ice_segments/fit_statistics/h_mean',\n",
      "            'gt1r/land_ice_segments/fit_statistics/h_mean',\n",
      "            'gt2l/land_ice_segments/fit_statistics/h_mean',\n",
      "            'gt2r/land_ice_segments/fit_statistics/h_mean',\n",
      "            'gt3l/land_ice_segments/fit_statistics/h_mean',\n",
      "            'gt3r/land_ice_segments/fit_statistics/h_mean'],\n",
      " 'h_rms_misfit': ['gt1l/land_ice_segments/fit_statistics/h_rms_misfit',\n",
      "                  'gt1r/land_ice_segments/fit_statistics/h_rms_misfit',\n",
      "                  'gt2l/land_ice_segments/fit_statistics/h_rms_misfit',\n",
      "                  'gt2r/land_ice_segments/fit_statistics/h_rms_misfit',\n",
      "                  'gt3l/land_ice_segments/fit_statistics/h_rms_misfit',\n",
      "                  'gt3r/land_ice_segments/fit_statistics/h_rms_misfit'],\n",
      " 'h_robust_sprd': ['gt1l/land_ice_segments/fit_statistics/h_robust_sprd',\n",
      "                   'gt1r/land_ice_segments/fit_statistics/h_robust_sprd',\n",
      "                   'gt2l/land_ice_segments/fit_statistics/h_robust_sprd',\n",
      "                   'gt2r/land_ice_segments/fit_statistics/h_robust_sprd',\n",
      "                   'gt3l/land_ice_segments/fit_statistics/h_robust_sprd',\n",
      "                   'gt3r/land_ice_segments/fit_statistics/h_robust_sprd'],\n",
      " 'latitude': ['gt1l/land_ice_segments/latitude',\n",
      "              'gt1r/land_ice_segments/latitude',\n",
      "              'gt2l/land_ice_segments/latitude',\n",
      "              'gt2r/land_ice_segments/latitude',\n",
      "              'gt3l/land_ice_segments/latitude',\n",
      "              'gt3r/land_ice_segments/latitude'],\n",
      " 'longitude': ['gt1l/land_ice_segments/longitude',\n",
      "               'gt1r/land_ice_segments/longitude',\n",
      "               'gt2l/land_ice_segments/longitude',\n",
      "               'gt2r/land_ice_segments/longitude',\n",
      "               'gt3l/land_ice_segments/longitude',\n",
      "               'gt3r/land_ice_segments/longitude'],\n",
      " 'n_fit_photons': ['gt1l/land_ice_segments/fit_statistics/n_fit_photons',\n",
      "                   'gt1r/land_ice_segments/fit_statistics/n_fit_photons',\n",
      "                   'gt2l/land_ice_segments/fit_statistics/n_fit_photons',\n",
      "                   'gt2r/land_ice_segments/fit_statistics/n_fit_photons',\n",
      "                   'gt3l/land_ice_segments/fit_statistics/n_fit_photons',\n",
      "                   'gt3r/land_ice_segments/fit_statistics/n_fit_photons'],\n",
      " 'r_eff': ['gt1l/land_ice_segments/geophysical/r_eff',\n",
      "           'gt1r/land_ice_segments/geophysical/r_eff',\n",
      "           'gt2l/land_ice_segments/geophysical/r_eff',\n",
      "           'gt2r/land_ice_segments/geophysical/r_eff',\n",
      "           'gt3l/land_ice_segments/geophysical/r_eff',\n",
      "           'gt3r/land_ice_segments/geophysical/r_eff'],\n",
      " 'sc_orient': ['orbit_info/sc_orient'],\n",
      " 'sc_orient_time': ['orbit_info/sc_orient_time'],\n",
      " 'seg_azimuth': ['gt1l/land_ice_segments/ground_track/seg_azimuth',\n",
      "                 'gt1r/land_ice_segments/ground_track/seg_azimuth',\n",
      "                 'gt2l/land_ice_segments/ground_track/seg_azimuth',\n",
      "                 'gt2r/land_ice_segments/ground_track/seg_azimuth',\n",
      "                 'gt3l/land_ice_segments/ground_track/seg_azimuth',\n",
      "                 'gt3r/land_ice_segments/ground_track/seg_azimuth'],\n",
      " 'segment_id': ['gt1l/land_ice_segments/segment_id',\n",
      "                'gt1l/segment_quality/segment_id',\n",
      "                'gt1r/land_ice_segments/segment_id',\n",
      "                'gt1r/segment_quality/segment_id',\n",
      "                'gt2l/land_ice_segments/segment_id',\n",
      "                'gt2l/segment_quality/segment_id',\n",
      "                'gt2r/land_ice_segments/segment_id',\n",
      "                'gt2r/segment_quality/segment_id',\n",
      "                'gt3l/land_ice_segments/segment_id',\n",
      "                'gt3l/segment_quality/segment_id',\n",
      "                'gt3r/land_ice_segments/segment_id',\n",
      "                'gt3r/segment_quality/segment_id'],\n",
      " 'sigma_geo_at': ['gt1l/land_ice_segments/ground_track/sigma_geo_at',\n",
      "                  'gt1r/land_ice_segments/ground_track/sigma_geo_at',\n",
      "                  'gt2l/land_ice_segments/ground_track/sigma_geo_at',\n",
      "                  'gt2r/land_ice_segments/ground_track/sigma_geo_at',\n",
      "                  'gt3l/land_ice_segments/ground_track/sigma_geo_at',\n",
      "                  'gt3r/land_ice_segments/ground_track/sigma_geo_at'],\n",
      " 'sigma_geo_h': ['gt1l/land_ice_segments/sigma_geo_h',\n",
      "                 'gt1r/land_ice_segments/sigma_geo_h',\n",
      "                 'gt2l/land_ice_segments/sigma_geo_h',\n",
      "                 'gt2r/land_ice_segments/sigma_geo_h',\n",
      "                 'gt3l/land_ice_segments/sigma_geo_h',\n",
      "                 'gt3r/land_ice_segments/sigma_geo_h'],\n",
      " 'sigma_geo_xt': ['gt1l/land_ice_segments/ground_track/sigma_geo_xt',\n",
      "                  'gt1r/land_ice_segments/ground_track/sigma_geo_xt',\n",
      "                  'gt2l/land_ice_segments/ground_track/sigma_geo_xt',\n",
      "                  'gt2r/land_ice_segments/ground_track/sigma_geo_xt',\n",
      "                  'gt3l/land_ice_segments/ground_track/sigma_geo_xt',\n",
      "                  'gt3r/land_ice_segments/ground_track/sigma_geo_xt'],\n",
      " 'signal_selection_source': ['gt1l/land_ice_segments/fit_statistics/signal_selection_source',\n",
      "                             'gt1l/segment_quality/signal_selection_source',\n",
      "                             'gt1r/land_ice_segments/fit_statistics/signal_selection_source',\n",
      "                             'gt1r/segment_quality/signal_selection_source',\n",
      "                             'gt2l/land_ice_segments/fit_statistics/signal_selection_source',\n",
      "                             'gt2l/segment_quality/signal_selection_source',\n",
      "                             'gt2r/land_ice_segments/fit_statistics/signal_selection_source',\n",
      "                             'gt2r/segment_quality/signal_selection_source',\n",
      "                             'gt3l/land_ice_segments/fit_statistics/signal_selection_source',\n",
      "                             'gt3l/segment_quality/signal_selection_source',\n",
      "                             'gt3r/land_ice_segments/fit_statistics/signal_selection_source',\n",
      "                             'gt3r/segment_quality/signal_selection_source'],\n",
      " 'snr_significance': ['gt1l/land_ice_segments/fit_statistics/snr_significance',\n",
      "                      'gt1r/land_ice_segments/fit_statistics/snr_significance',\n",
      "                      'gt2l/land_ice_segments/fit_statistics/snr_significance',\n",
      "                      'gt2r/land_ice_segments/fit_statistics/snr_significance',\n",
      "                      'gt3l/land_ice_segments/fit_statistics/snr_significance',\n",
      "                      'gt3r/land_ice_segments/fit_statistics/snr_significance'],\n",
      " 'start_delta_time': ['ancillary_data/start_delta_time'],\n",
      " 'tide_ocean': ['gt1l/land_ice_segments/geophysical/tide_ocean',\n",
      "                'gt1r/land_ice_segments/geophysical/tide_ocean',\n",
      "                'gt2l/land_ice_segments/geophysical/tide_ocean',\n",
      "                'gt2r/land_ice_segments/geophysical/tide_ocean',\n",
      "                'gt3l/land_ice_segments/geophysical/tide_ocean',\n",
      "                'gt3r/land_ice_segments/geophysical/tide_ocean'],\n",
      " 'w_surface_window_final': ['gt1l/land_ice_segments/fit_statistics/w_surface_window_final',\n",
      "                            'gt1r/land_ice_segments/fit_statistics/w_surface_window_final',\n",
      "                            'gt2l/land_ice_segments/fit_statistics/w_surface_window_final',\n",
      "                            'gt2r/land_ice_segments/fit_statistics/w_surface_window_final',\n",
      "                            'gt3l/land_ice_segments/fit_statistics/w_surface_window_final',\n",
      "                            'gt3r/land_ice_segments/fit_statistics/w_surface_window_final'],\n",
      " 'x_atc': ['gt1l/land_ice_segments/ground_track/x_atc',\n",
      "           'gt1r/land_ice_segments/ground_track/x_atc',\n",
      "           'gt2l/land_ice_segments/ground_track/x_atc',\n",
      "           'gt2r/land_ice_segments/ground_track/x_atc',\n",
      "           'gt3l/land_ice_segments/ground_track/x_atc',\n",
      "           'gt3r/land_ice_segments/ground_track/x_atc'],\n",
      " 'y_atc': ['gt1l/land_ice_segments/ground_track/y_atc',\n",
      "           'gt1r/land_ice_segments/ground_track/y_atc',\n",
      "           'gt2l/land_ice_segments/ground_track/y_atc',\n",
      "           'gt2r/land_ice_segments/ground_track/y_atc',\n",
      "           'gt3l/land_ice_segments/ground_track/y_atc',\n",
      "           'gt3r/land_ice_segments/ground_track/y_atc']}\n"
     ]
    }
   ],
   "source": [
    "region_a.order_vars.remove(all=True)\n",
    "region_a.order_vars.append(defaults=True)\n",
    "pprint(region_a.order_vars.wanted)\n",
    "# print(region_a.order_vars.wanted['latitude'])\n",
    "# print(region_a.order_vars.wanted['longitude'])\n",
    "# print(region_a.order_vars.wanted['h_li'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "source": [
    "lets stick with default order variables for now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of data order requests is  2  for  16  granules.\n",
      "Data request  1  of  2  is submitting to NSIDC\n",
      "order ID:  5000000699965\n",
      "Initial status of your order request at NSIDC is:  processing\n",
      "Your order status is still  processing  at NSIDC. Please continue waiting... this may take a few moments.\n",
      "Your order is:  complete_with_errors\n",
      "NSIDC provided these error messages:\n",
      "['178109382:NoMatchingData - No data found that matched subset constraints. '\n",
      " 'Exit code 3.',\n",
      " 'PT5.650S',\n",
      " 'ICESAT2']\n",
      "Your order is: complete_with_errors\n",
      "Data request  2  of  2  is submitting to NSIDC\n",
      "order ID:  5000000699966\n",
      "Initial status of your order request at NSIDC is:  processing\n",
      "Your order status is still  processing  at NSIDC. Please continue waiting... this may take a few moments.\n",
      "Your order is:  complete_with_errors\n",
      "NSIDC provided these error messages:\n",
      "['178110281:NoMatchingData - No data found that matched subset constraints. '\n",
      " 'Exit code 3.',\n",
      " '178105671:NoMatchingData - No data found that matched subset constraints. '\n",
      " 'Exit code 3.',\n",
      " 'PT4.176S',\n",
      " 'ICESAT2']\n",
      "Your order is: complete_with_errors\n"
     ]
    }
   ],
   "source": [
    "# Without variable subsetting, or with variable subsetting if you have run region_a.subsetparams(Coverage=region_a.order_vars.wanted):\n",
    "# region_a.order_granules()\n",
    "\n",
    "# # With variable subsetting, if this is your first use of the Coverage keyword argument (kwarg):\n",
    "region_a.order_granules(Coverage=region_a.order_vars.wanted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning download of zipped output...\n",
      "Data request 5000000699965 of  2  order(s) is downloaded.\n",
      "Beginning download of zipped output...\n",
      "Data request 5000000699966 of  2  order(s) is downloaded.\n",
      "Download complete\n"
     ]
    }
   ],
   "source": [
    "# !mkdir ~/downloads\n",
    "\n",
    "path = '/home/jovyan/downloads'\n",
    "\n",
    "# without variable subsetting, or with variable subsetting if you have run region_a.order_granules(Coverage=region_a.order_vars.wanted)\n",
    "region_a.download_granules(path)\n",
    "\n",
    "# with variable subsetting, if this is your first use of the Coverage keyword argument (kwarg)\n",
    "# region_a.download_granules(Coverage=region_a.order_vars.wanted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# look at the data, starting with the demo for hdf5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pull out x,y,z,t and whatever else was needed for Johan's tutorial; try and follow it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product:     ATL06\n",
      "dates:       ['2019-06-18', '2019-06-25']\n",
      "start time:  00:00:00\n",
      "end time:    23:59:59\n",
      "version:     003\n",
      "extent:      ['bounding box', [-162, -85, -147, -83]]\n",
      "\n",
      "DATA:\n",
      "('Number of available granules', 16)\n",
      "('Average size of granules (MB)', 92.93979632854376)\n",
      "('Total size of all granules (MB)', 1487.0367412567002)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('product:    ', region_a.dataset)\n",
    "print('dates:      ', region_a.dates)\n",
    "print('start time: ', region_a.start_time)\n",
    "print('end time:   ', region_a.end_time)\n",
    "print('version:    ', region_a.dataset_version)\n",
    "print('extent:     ', region_a.spatial_extent)\n",
    "\n",
    "print('\\nDATA:')\n",
    "print('\\n'.join([str(item) for item in region_a.avail_granules().items()]))\n",
    "\n",
    "region_a.visualize_spatial_extent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/downloads/processed_ATL06_20190623142654_13180311_003_01.h5\n",
      "/home/jovyan/downloads/processed_ATL06_20190623001818_13090311_003_01.h5\n",
      "/home/jovyan/downloads/processed_ATL06_20190619002638_12480311_003_01.h5\n",
      "/home/jovyan/downloads/processed_ATL06_20190621010937_12790311_003_01.h5\n",
      "/home/jovyan/downloads/processed_ATL06_20190623235238_13240311_003_01.h5\n",
      "/home/jovyan/downloads/processed_ATL06_20190625150952_13490311_003_01.h5\n",
      "/home/jovyan/downloads/processed_ATL06_20190618150054_12420311_003_01.h5\n",
      "/home/jovyan/downloads/processed_ATL06_20190618005217_12330311_003_01.h5\n",
      "/home/jovyan/downloads/processed_ATL06_20190621151813_12880311_003_01.h5\n",
      "/home/jovyan/downloads/processed_ATL06_20190620154352_12730311_003_01.h5\n",
      "/                        Group\n",
      "/METADATA                Group\n",
      "/METADATA/AcquisitionInformation Group\n",
      "/METADATA/AcquisitionInformation/lidar Group\n",
      "/METADATA/AcquisitionInformation/lidarDocument Group\n",
      "/METADATA/AcquisitionInformation/platform Group\n",
      "/METADATA/AcquisitionInformation/platformDocument Group\n",
      "/METADATA/DataQuality    Group\n",
      "/METADATA/DataQuality/CompletenessOmission Group\n",
      "/METADATA/DataQuality/DomainConsistency Group\n",
      "/METADATA/DatasetIdentification Group\n",
      "/METADATA/Extent         Group\n",
      "/METADATA/Lineage        Group\n",
      "/METADATA/Lineage/ANC06-01 Group\n",
      "/METADATA/Lineage/ANC06-02 Group\n",
      "/METADATA/Lineage/ANC06-03 Group\n",
      "/METADATA/Lineage/ANC17  Group\n",
      "/METADATA/Lineage/ANC19  Group\n",
      "/METADATA/Lineage/ANC25-06 Group\n",
      "/METADATA/Lineage/ANC26-06 Group\n",
      "/METADATA/Lineage/ANC28  Group\n",
      "/METADATA/Lineage/ANC36-06 Group\n",
      "/METADATA/Lineage/ANC38-06 Group\n",
      "/METADATA/Lineage/ATL03  Group\n",
      "/METADATA/Lineage/ATL09  Group\n",
      "/METADATA/Lineage/Control Group\n",
      "/METADATA/ProcessStep    Group\n",
      "/METADATA/ProcessStep/Browse Group\n",
      "/METADATA/ProcessStep/Metadata Group\n",
      "/METADATA/ProcessStep/PGE Group\n",
      "/METADATA/ProcessStep/QA Group\n",
      "/METADATA/ProductSpecificationDocument Group\n",
      "/METADATA/QADatasetIdentification Group\n",
      "/METADATA/SeriesIdentification Group\n",
      "/ancillary_data          Group\n",
      "/ancillary_data/atlas_sdp_gps_epoch Dataset {1}\n",
      "/ancillary_data/data_end_utc Dataset {1}\n",
      "/ancillary_data/data_start_utc Dataset {1}\n",
      "/ancillary_data/end_delta_time Dataset {1}\n",
      "/ancillary_data/granule_end_utc Dataset {1}\n",
      "/ancillary_data/granule_start_utc Dataset {1}\n",
      "/ancillary_data/start_delta_time Dataset {1}\n",
      "/gt1l                    Group\n",
      "/gt1l/land_ice_segments  Group\n",
      "/gt1l/land_ice_segments/atl06_quality_summary Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/delta_time Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics Group\n",
      "/gt1l/land_ice_segments/fit_statistics/dh_fit_dx Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/dh_fit_dy Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/h_mean Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/h_rms_misfit Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/h_robust_sprd Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/n_fit_photons Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/signal_selection_source Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/snr_significance Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/w_surface_window_final Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/geophysical Group\n",
      "/gt1l/land_ice_segments/geophysical/bsnow_conf Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/bsnow_h Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/cloud_flg_asr Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/cloud_flg_atm Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/r_eff Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/tide_ocean Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/ground_track Group\n",
      "/gt1l/land_ice_segments/ground_track/seg_azimuth Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/sigma_geo_at Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/sigma_geo_xt Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/x_atc Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/y_atc Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/h_li Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/h_li_sigma Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/latitude Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/longitude Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/segment_id Dataset {2545/Inf}\n",
      "/gt1l/land_ice_segments/sigma_geo_h Dataset {2545/Inf}\n",
      "/gt1l/residual_histogram Group\n",
      "/gt1l/residual_histogram/delta_time Dataset {254/Inf}\n",
      "/gt1l/segment_quality    Group\n",
      "/gt1l/segment_quality/delta_time Dataset {2545/Inf}\n",
      "/gt1l/segment_quality/segment_id Dataset {2545/Inf}\n",
      "/gt1l/segment_quality/signal_selection_source Dataset {2545/Inf}\n",
      "/gt1r                    Group\n",
      "/gt1r/land_ice_segments  Group\n",
      "/gt1r/land_ice_segments/atl06_quality_summary Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/delta_time Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics Group\n",
      "/gt1r/land_ice_segments/fit_statistics/dh_fit_dx Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/dh_fit_dy Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/h_mean Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/h_rms_misfit Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/h_robust_sprd Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/n_fit_photons Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/signal_selection_source Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/snr_significance Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/w_surface_window_final Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/geophysical Group\n",
      "/gt1r/land_ice_segments/geophysical/bsnow_conf Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/bsnow_h Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/cloud_flg_asr Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/cloud_flg_atm Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/r_eff Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/tide_ocean Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/ground_track Group\n",
      "/gt1r/land_ice_segments/ground_track/seg_azimuth Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/sigma_geo_at Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/sigma_geo_xt Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/x_atc Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/y_atc Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/h_li Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/h_li_sigma Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/latitude Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/longitude Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/segment_id Dataset {2558/Inf}\n",
      "/gt1r/land_ice_segments/sigma_geo_h Dataset {2558/Inf}\n",
      "/gt1r/residual_histogram Group\n",
      "/gt1r/residual_histogram/delta_time Dataset {256/Inf}\n",
      "/gt1r/segment_quality    Group\n",
      "/gt1r/segment_quality/delta_time Dataset {2558/Inf}\n",
      "/gt1r/segment_quality/segment_id Dataset {2558/Inf}\n",
      "/gt1r/segment_quality/signal_selection_source Dataset {2558/Inf}\n",
      "/gt2l                    Group\n",
      "/gt2l/land_ice_segments  Group\n",
      "/gt2l/land_ice_segments/atl06_quality_summary Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/delta_time Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics Group\n",
      "/gt2l/land_ice_segments/fit_statistics/dh_fit_dx Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/dh_fit_dy Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/h_mean Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/h_rms_misfit Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/h_robust_sprd Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/n_fit_photons Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/signal_selection_source Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/snr_significance Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/w_surface_window_final Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/geophysical Group\n",
      "/gt2l/land_ice_segments/geophysical/bsnow_conf Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/bsnow_h Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/cloud_flg_asr Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/cloud_flg_atm Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/r_eff Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/tide_ocean Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/ground_track Group\n",
      "/gt2l/land_ice_segments/ground_track/seg_azimuth Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/sigma_geo_at Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/sigma_geo_xt Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/x_atc Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/y_atc Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/h_li Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/h_li_sigma Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/latitude Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/longitude Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/segment_id Dataset {2679/Inf}\n",
      "/gt2l/land_ice_segments/sigma_geo_h Dataset {2679/Inf}\n",
      "/gt2l/residual_histogram Group\n",
      "/gt2l/residual_histogram/delta_time Dataset {275/Inf}\n",
      "/gt2l/segment_quality    Group\n",
      "/gt2l/segment_quality/delta_time Dataset {2679/Inf}\n",
      "/gt2l/segment_quality/segment_id Dataset {2679/Inf}\n",
      "/gt2l/segment_quality/signal_selection_source Dataset {2679/Inf}\n",
      "/gt2r                    Group\n",
      "/gt2r/land_ice_segments  Group\n",
      "/gt2r/land_ice_segments/atl06_quality_summary Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/delta_time Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics Group\n",
      "/gt2r/land_ice_segments/fit_statistics/dh_fit_dx Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/dh_fit_dy Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/h_mean Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/h_rms_misfit Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/h_robust_sprd Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/n_fit_photons Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/signal_selection_source Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/snr_significance Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/w_surface_window_final Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/geophysical Group\n",
      "/gt2r/land_ice_segments/geophysical/bsnow_conf Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/bsnow_h Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/cloud_flg_asr Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/cloud_flg_atm Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/r_eff Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/tide_ocean Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/ground_track Group\n",
      "/gt2r/land_ice_segments/ground_track/seg_azimuth Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/sigma_geo_at Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/sigma_geo_xt Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/x_atc Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/y_atc Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/h_li Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/h_li_sigma Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/latitude Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/longitude Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/segment_id Dataset {2681/Inf}\n",
      "/gt2r/land_ice_segments/sigma_geo_h Dataset {2681/Inf}\n",
      "/gt2r/residual_histogram Group\n",
      "/gt2r/residual_histogram/delta_time Dataset {275/Inf}\n",
      "/gt2r/segment_quality    Group\n",
      "/gt2r/segment_quality/delta_time Dataset {2681/Inf}\n",
      "/gt2r/segment_quality/segment_id Dataset {2681/Inf}\n",
      "/gt2r/segment_quality/signal_selection_source Dataset {2681/Inf}\n",
      "/gt3l                    Group\n",
      "/gt3l/land_ice_segments  Group\n",
      "/gt3l/land_ice_segments/atl06_quality_summary Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/delta_time Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics Group\n",
      "/gt3l/land_ice_segments/fit_statistics/dh_fit_dx Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/dh_fit_dy Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/h_mean Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/h_rms_misfit Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/h_robust_sprd Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/n_fit_photons Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/signal_selection_source Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/snr_significance Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/w_surface_window_final Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/geophysical Group\n",
      "/gt3l/land_ice_segments/geophysical/bsnow_conf Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/bsnow_h Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/cloud_flg_asr Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/cloud_flg_atm Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/r_eff Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/tide_ocean Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/ground_track Group\n",
      "/gt3l/land_ice_segments/ground_track/seg_azimuth Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/sigma_geo_at Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/sigma_geo_xt Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/x_atc Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/y_atc Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/h_li Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/h_li_sigma Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/latitude Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/longitude Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/segment_id Dataset {2422/Inf}\n",
      "/gt3l/land_ice_segments/sigma_geo_h Dataset {2422/Inf}\n",
      "/gt3l/residual_histogram Group\n",
      "/gt3l/residual_histogram/delta_time Dataset {244/Inf}\n",
      "/gt3l/segment_quality    Group\n",
      "/gt3l/segment_quality/delta_time Dataset {2422/Inf}\n",
      "/gt3l/segment_quality/segment_id Dataset {2422/Inf}\n",
      "/gt3l/segment_quality/signal_selection_source Dataset {2422/Inf}\n",
      "/gt3r                    Group\n",
      "/gt3r/land_ice_segments  Group\n",
      "/gt3r/land_ice_segments/atl06_quality_summary Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/delta_time Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics Group\n",
      "/gt3r/land_ice_segments/fit_statistics/dh_fit_dx Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/dh_fit_dy Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/h_mean Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/h_rms_misfit Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/h_robust_sprd Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/n_fit_photons Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/signal_selection_source Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/snr_significance Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/w_surface_window_final Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/geophysical Group\n",
      "/gt3r/land_ice_segments/geophysical/bsnow_conf Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/bsnow_h Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/cloud_flg_asr Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/cloud_flg_atm Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/r_eff Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/tide_ocean Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/ground_track Group\n",
      "/gt3r/land_ice_segments/ground_track/seg_azimuth Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/sigma_geo_at Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/sigma_geo_xt Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/x_atc Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/y_atc Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/h_li Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/h_li_sigma Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/latitude Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/longitude Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/segment_id Dataset {2424/Inf}\n",
      "/gt3r/land_ice_segments/sigma_geo_h Dataset {2424/Inf}\n",
      "/gt3r/residual_histogram Group\n",
      "/gt3r/residual_histogram/delta_time Dataset {244/Inf}\n",
      "/gt3r/segment_quality    Group\n",
      "/gt3r/segment_quality/delta_time Dataset {2424/Inf}\n",
      "/gt3r/segment_quality/segment_id Dataset {2424/Inf}\n",
      "/gt3r/segment_quality/signal_selection_source Dataset {2424/Inf}\n",
      "/orbit_info              Group\n",
      "/orbit_info/sc_orient    Dataset {1/Inf}\n",
      "/orbit_info/sc_orient_time Dataset {1/Inf}\n",
      "/quality_assessment      Group\n",
      "/quality_assessment/gt1l Group\n",
      "/quality_assessment/gt1l/delta_time Dataset {243/Inf}\n",
      "/quality_assessment/gt1r Group\n",
      "/quality_assessment/gt1r/delta_time Dataset {243/Inf}\n",
      "/quality_assessment/gt2l Group\n",
      "/quality_assessment/gt2l/delta_time Dataset {242/Inf}\n",
      "/quality_assessment/gt2r Group\n",
      "/quality_assessment/gt2r/delta_time Dataset {242/Inf}\n",
      "/quality_assessment/gt3l Group\n",
      "/quality_assessment/gt3l/delta_time Dataset {242/Inf}\n",
      "/quality_assessment/gt3r Group\n",
      "/quality_assessment/gt3r/delta_time Dataset {242/Inf}\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import h5py\n",
    "\n",
    "\n",
    "# look at a file\n",
    "data_home = Path(path)\n",
    "files = list(data_home.glob('*.h5'))\n",
    "\n",
    "for f in files[:10]: print(f)\n",
    "# print('Total number of files:', len(files))\n",
    "\n",
    "!h5ls -r {files[4]} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processed_ATL06_20190618005217_12330311_003_01.h5\n",
      "processed_ATL06_20190618150054_12420311_003_01.h5\n",
      "processed_ATL06_20190619002638_12480311_003_01.h5\n",
      "processed_ATL06_20190619143514_12570311_003_01.h5\n",
      "processed_ATL06_20190620000058_12630311_003_01.h5\n",
      "processed_ATL06_20190620154352_12730311_003_01.h5\n",
      "processed_ATL06_20190621010937_12790311_003_01.h5\n",
      "processed_ATL06_20190621151813_12880311_003_01.h5\n",
      "processed_ATL06_20190622004357_12940311_003_01.h5\n",
      "processed_ATL06_20190623001818_13090311_003_01.h5\n",
      "processed_ATL06_20190623142654_13180311_003_01.h5\n",
      "processed_ATL06_20190623235238_13240311_003_01.h5\n",
      "processed_ATL06_20190625150952_13490311_003_01.h5\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "utility funcitons:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyproj\n",
    "from astropy.time import Time\n",
    "\n",
    "def gps2dyr(time):\n",
    "    \"\"\"Converte GPS time to decimal years.\"\"\"\n",
    "    return Time(time, format='gps').decimalyear\n",
    "\n",
    "\n",
    "def orbit_type(time, lat, tmax=1):\n",
    "    \"\"\"Separate tracks into ascending and descending.\n",
    "    \n",
    "    Defines tracks as segments with time breaks > tmax,\n",
    "    and tests whether lat increases or decreases w/time.\n",
    "    \"\"\"\n",
    "    tracks = np.zeros(lat.shape)  # generate track segment\n",
    "    tracks[0:np.argmax(np.abs(lat))] = 1  # set values for segment\n",
    "    is_asc = np.zeros(tracks.shape, dtype=bool)  # output index array\n",
    "\n",
    "    # Loop trough individual secments\n",
    "    for track in np.unique(tracks):\n",
    "    \n",
    "        i_track, = np.where(track == tracks)  # get all pts from seg\n",
    "    \n",
    "        if len(i_track) < 2: continue\n",
    "    \n",
    "        # Test if lat increases (asc) or decreases (des) w/time\n",
    "        i_min = time[i_track].argmin()\n",
    "        i_max = time[i_track].argmax()\n",
    "        lat_diff = lat[i_track][i_max] - lat[i_track][i_min]\n",
    "    \n",
    "        # Determine track type\n",
    "        if lat_diff > 0:  is_asc[i_track] = True\n",
    "    \n",
    "    return is_asc\n",
    "\n",
    "\n",
    "def transform_coord(proj1, proj2, x, y):\n",
    "    \"\"\"Transform coordinates from proj1 to proj2 (EPSG num).\n",
    "\n",
    "    Example EPSG projections:\n",
    "        Geodetic (lon/lat): 4326\n",
    "        Polar Stereo AnIS (x/y): 3031\n",
    "        Polar Stereo GrIS (x/y): 3413\n",
    "    \"\"\"\n",
    "    # Set full EPSG projection strings\n",
    "    proj1 = pyproj.Proj(\"+init=EPSG:\"+str(proj1))\n",
    "    proj2 = pyproj.Proj(\"+init=EPSG:\"+str(proj2))\n",
    "    return pyproj.transform(proj1, proj2, x, y)  # convert\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simple reader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "\n",
    "def read_atl06(fname, outdir='data', bbox=None):\n",
    "    \"\"\"Read one ATL06 file and output 6 reduced files. \n",
    "    \n",
    "    Extract variables of interest and separate the ATL06 file \n",
    "    into each beam (ground track) and ascending/descending orbits.\n",
    "    \"\"\"\n",
    "\n",
    "    # Each beam is a group\n",
    "    group = ['/gt1l', '/gt1r', '/gt2l', '/gt2r', '/gt3l', '/gt3r']\n",
    "\n",
    "    # Loop trough beams\n",
    "    for k, g in enumerate(group):\n",
    "    \n",
    "        #-----------------------------------#\n",
    "        # 1) Read in data for a single beam #\n",
    "        #-----------------------------------#\n",
    "        \n",
    "        data = {}\n",
    "    \n",
    "        try:\n",
    "            # Load vars into memory (include as many as you want)\n",
    "            with h5py.File(fname, 'r') as fi:\n",
    "                \n",
    "                data['lat'] = fi[g+'/land_ice_segments/latitude'][:]\n",
    "                data['lon'] = fi[g+'/land_ice_segments/longitude'][:]\n",
    "                data['h_li'] = fi[g+'/land_ice_segments/h_li'][:]\n",
    "                data['s_li'] = fi[g+'/land_ice_segments/h_li_sigma'][:]\n",
    "                data['t_dt'] = fi[g+'/land_ice_segments/delta_time'][:] # time elapsed from a reference time (below, t_ref)\n",
    "                data['q_flag'] = fi[g+'/land_ice_segments/atl06_quality_summary'][:]\n",
    "                data['s_fg'] = fi[g+'/land_ice_segments/fit_statistics/signal_selection_source'][:]\n",
    "                data['snr'] = fi[g+'/land_ice_segments/fit_statistics/snr_significance'][:]\n",
    "                data['h_rb'] = fi[g+'/land_ice_segments/fit_statistics/h_robust_sprd'][:]\n",
    "                data['dac'] = fi[g+'/land_ice_segments/geophysical/dac'][:]\n",
    "                data['f_sn'] = fi[g+'/land_ice_segments/geophysical/bsnow_conf'][:]\n",
    "                data['dh_fit_dx'] = fi[g+'/land_ice_segments/fit_statistics/dh_fit_dx'][:]\n",
    "                data['tide_earth'] = fi[g+'/land_ice_segments/geophysical/tide_earth'][:]\n",
    "                data['tide_load'] = fi[g+'/land_ice_segments/geophysical/tide_load'][:]\n",
    "                data['tide_ocean'] = fi[g+'/land_ice_segments/geophysical/tide_ocean'][:]\n",
    "                data['tide_pole'] = fi[g+'/land_ice_segments/geophysical/tide_pole'][:]\n",
    "                \n",
    "                rgt = fi['/orbit_info/rgt'][:]                           # single value\n",
    "                t_ref = fi['/ancillary_data/atlas_sdp_gps_epoch'][:]     # single value # reference time\n",
    "                beam_type = fi[g].attrs[\"atlas_beam_type\"].decode()      # strong/weak (str)\n",
    "                spot_number = fi[g].attrs[\"atlas_spot_number\"].decode()  # number (str)\n",
    "                \n",
    "        except:\n",
    "            print('skeeping group:', g)\n",
    "            print('in file:', fname)\n",
    "            continue\n",
    "            \n",
    "        #---------------------------------------------#\n",
    "        # 2) Filter data according region and quality #\n",
    "        #---------------------------------------------#\n",
    "        \n",
    "        # Select a region of interest\n",
    "        if bbox:\n",
    "            lonmin, lonmax, latmin, latmax = bbox\n",
    "            bbox_mask = (data['lon'] >= lonmin) & (data['lon'] <= lonmax) & \\\n",
    "                        (data['lat'] >= latmin) & (data['lat'] <= latmax)\n",
    "        else:\n",
    "            bbox_mask = np.ones_like(data['lat'], dtype=bool)  # get all\n",
    "            \n",
    "        # Only keep good data (quality flag + threshold + bbox) # keep only data less than 10000m high (if you are into surface features and not clouds)\n",
    "        mask = (data['q_flag'] == 0) & (np.abs(data['h_li']) < 10e3) & (bbox_mask == 1)\n",
    "        \n",
    "        # If no data left, skeep\n",
    "        if not any(mask): continue\n",
    "        \n",
    "        # Update data variables\n",
    "        for k, v in data.items(): data[k] = v[mask]\n",
    "            \n",
    "        #----------------------------------------------------#\n",
    "        # 3) Convert time, separate tracks, reproject coords #\n",
    "        #----------------------------------------------------#\n",
    "        \n",
    "        # Time in GPS seconds (secs sinde Jan 5, 1980)\n",
    "        t_gps = t_ref + data['t_dt']\n",
    "\n",
    "        # Time in decimal years\n",
    "        t_year = gps2dyr(t_gps)\n",
    "\n",
    "        # Determine orbit type\n",
    "        is_asc = orbit_type(t_year, data['lat'])\n",
    "        \n",
    "        # Geodetic lon/lat -> Polar Stereo x/y\n",
    "        x, y = transform_coord(4326, 3031, data['lon'], data['lat'])\n",
    "        \n",
    "        data['x'] = x\n",
    "        data['y'] = y\n",
    "        data['t_gps'] = t_gps\n",
    "        data['t_year'] = t_year\n",
    "        data['is_asc'] = is_asc\n",
    "        \n",
    "        #-----------------------#\n",
    "        # 4) Save selected data #\n",
    "        #-----------------------#\n",
    "        \n",
    "        # Define output dir and file\n",
    "        outdir = Path(outdir)    \n",
    "        fname = Path(fname)\n",
    "        outdir.mkdir(exist_ok=True)\n",
    "        outfile = outdir / fname.name.replace('.h5', '_' + g[1:] + '.h5')\n",
    "        \n",
    "        # Save variables\n",
    "        with h5py.File(outfile, 'w') as fo:\n",
    "            for k, v in data.items(): fo[k] = v\n",
    "            print('out ->', outfile)\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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